An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions

The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

The Application of Weighted Association Rules in Host-Based Intrusion Detection System

Association rules are useful for determining correlations between items. Applying association rules to intrusion detection system (IDS) can improve the detection rate, but false positive rate is also increased. Weighted association rules are used in this paper to mine intrustion models, which can increase the detection rate and decrease the false positive rate by some extent. Based on this, the structure of host-based IDS using weighted association rules is proposed.

Deep Learning-Based Hybrid Intelligent Intrusion Detection System

Machine learning(ML)algorithms are often used to design effective intrusion detection(ID)systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels.However,cybersecurity attacks are still increasing.An ID system can play a vital role in detecting such threats.Existing ID systems are unable to detect malicious threats,primarily because they adopt approaches that are based on traditional ML techniques,which are less concerned with the accurate classication and feature selection.Thus,developing an accurate and intelligent ID system is a priority.The main objective of this study was to develop a hybrid intelligent intrusion detection system(HIIDS)to learn crucial features representation efciently and automatically from massive unlabeled raw network trafc data.Many ID datasets are publicly available to the cybersecurity research community.As such,we used a spark MLlib(machine learning library)-based robust classier,such as logistic regression(LR),extreme gradient boosting(XGB)was used for anomaly detection,and a state-of-the-art DL,such as a long short-term memory autoencoder(LSTMAE)for misuse attack was used to develop an efcient and HIIDS to detect and classify unpredictable attacks.Our approach utilized LSTM to detect temporal features and an AE to more efciently detect global features.Therefore,to evaluate the efcacy of our proposed approach,experiments were conducted on a publicly existing dataset,the contemporary real-life ISCX-UNB dataset.The simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS signicantly outperformed existing ID approaches,achieving a high accuracy rate of up to 97.52%for the ISCX-UNB dataset respectively 10-fold crossvalidation test.It is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale.

Intrusion Detection System for PS-Poll DoS Attack in 802.11 Networks Using Real Time Discrete Event System

Wi-Fi devices have limited battery life because of which conserving battery life is imperative. The 802.11 Wi-Fi standard provides power management feature that allows stations(STAs) to enter into sleep state to preserve energy without any frame losses. After the STA wakes up, it sends a null data or PS-Poll frame to retrieve frame(s) buffered by the access point(AP), if any during its sleep period. An attacker can launch a power save denial of service(PS-DoS) attack on the sleeping STA(s) by transmitting a spoofed null data or PS-Poll frame(s) to retrieve the buffered frame(s) of the sleeping STA(s) from the AP causing frame losses for the targeted STA(s). Current approaches to prevent or detect the PS-DoS attack require encryption,change in protocol or installation of proprietary hardware. These solutions suffer from expensive setup, maintenance, scalability and deployment issues. The PS-DoS attack does not differ in semantics or statistics under normal and attack circumstances.So signature and anomaly based intrusion detection system(IDS) are unfit to detect the PS-DoS attack. In this paper we propose a timed IDS based on real time discrete event system(RTDES) for detecting PS-DoS attack. The proposed DES based IDS overcomes the drawbacks of existing systems and detects the PS-DoS attack with high accuracy and detection rate. The correctness of the RTDES based IDS is proved by experimenting all possible attack scenarios.

A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

In recent years,cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things(IoT)and the widespread development of computer infrastructure and systems.It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security.Researchers have worked on developing intrusion detection models that depend on machine learning(ML)methods to address these security problems.An intelligent intrusion detection device powered by data can exploit artificial intelligence(AI),and especially ML,techniques.Accordingly,we propose in this article an intrusion detection model based on a Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System(RTS-DELM-CSIDS)security model.The proposed model initially determines the rating of security aspects contributing to their significance and then develops a comprehensive intrusion detection framework focused on the essential characteristics.Furthermore,we investigated the feasibility of our proposed RTS-DELM-CSIDS framework by performing dataset evaluations and calculating accuracy parameters to validate.The experimental findings demonstrate that the RTS-DELM-CSIDS framework outperforms conventional algorithms.Furthermore,the proposed approach has not only research significance but also practical significance.

An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

The success of Internet of Things(IoT)deployment has emerged important smart applications.These applications are running independently on different platforms,almost everywhere in the world.Internet of Medical Things(IoMT),also referred as the healthcare Internet of Things,is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems.Indeed,with the impact of the COVID-19 pandemic,a large number of interconnected devices designed to create smart networks.These networks monitor patients from remote locations as well as tracking medication orders.However,IoT may be jeopardized by attacks such as TCP SYN flooding and sinkhole attacks.In this paper,we address the issue of detecting Denial of Service attacks performed by TCP SYN flooding attacker nodes.For this purpose,we develop a new algorithm for Intrusion Detection System(IDS)to detect malicious activities in the Internet of Medical Things.The proposed scheme minimizes as possible the number of attacks to ensure data security,and preserve confidentiality of gathered data.In order to check the viability of our approach,we evaluate analytically and via simulations the performance of our proposed solution under different probability of attacks.

A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked Autoencoder

Cyber physical systems(CPSs)are a networked system of cyber(computation,communication)and physical(sensors,actuators)elements that interact in a feedback loop with the assistance of human interference.Generally,CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices.Increased utilization of CPSs,however,poses many threats,which may be of major significance for users.Such security issues in CPSs represent a global issue;therefore,developing a robust,secure,and effective CPS is currently a hot research topic.To resolve this issue,an intrusion detection system(IDS)can be designed to protect CPSs.When the IDS detects an anomaly,it instantly takes the necessary actions to avoid harming the system.In this study,we introduce a new parameter-tuned deep-stacked autoencoder based on deep learning(DL),called PT-DSAE,for the IDS in CPSs.The proposed model involves preprocessing,feature extraction,parameter tuning,and classification.First,data preprocessing takes place to eliminate the noise present in the data.Next,a DL-based DSAE model is applied to detect anomalies in the CPS.In addition,hyperparameter tuning of the DSAE takes place using a search-and-rescue optimization algorithm to tune the parameters of the DSAE,such as the number of hidden layers,batch size,epoch count,and learning rate.To assess the experimental outcomes of the PT-DSAE model,a series of experiments were performed using data from a sensor-based CPS.Moreover,a detailed comparative analysis was performed to ensure the effective detection outcome of the PT-DSAE technique.The experimental results obtained verified the superior performance on the applied data over the compared methods.

Protecting Against Address Space Layout Randomisation (ASLR) Compromises and Return-to-Libc Attacks Using Network Intrusion Detection Systems

Writable XOR executable （W⊕X） and address space layout randomisation （ASLR） have elevated the understanding necessary to perpetrate buffer overflow exploits [1] . However, they have not proved to be a panacea [1 3] , and so other mechanisms, such as stack guards and prelinking, have been introduced. In this paper, we show that host-based protection still does not offer a complete solution. To demonstrate the protection inadequacies, we perform an over the network brute force return-to-libc attack against a preforking concurrent server to gain remote access to a shell. The attack defeats host protection including W⊕X and ASLR. We then demonstrate that deploying a network intrusion detection systems （NIDS） with appropriate signatures can detect this attack efficiently.

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion detection.Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks.This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis techniques.This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions.Specifically,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement methods.The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment.Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities.So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment.The main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these networks.So it is very crucial to design an IDS specifically to target on the wireless networks.

Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances,Challenges and Future Directions

Software-Defined Networking(SDN)enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions.Recently Machine Learning(ML)techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems(IDSs)considering logically centralized control and global view of the network provided by SDN.Many IDSs have developed using advances in machine learning and deep learning.This study presents a comprehensive review of recent work ofML-based IDS in context to SDN.It presents a comprehensive study of the existing review papers in the field.It is followed by introducing intrusion detection,ML techniques and their types.Specifically,we present a systematic study of recent works,discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN,and promising future works in this field.

AN INTRUSION DETECTION SYSTEM BASED ON EVIDENCE THEORY AND ROUGH SET THEORY

In this paper,we propose a novel Intrusion Detection System (IDS) architecture utilizing both the evidence theory and Rough Set Theory (RST). Evidence theory is an effective tool in dealing with uncertainty question. It relies on the expert knowledge to provide evidences,needing the evidences to be independent,and this make it difficult in application. To solve this problem,a hybrid system of rough sets and evidence theory is proposed. Firstly,simplification are made based on Variable Precision Rough Set (VPRS) conditional entropy. Thus,the Basic Belief Assignment (BBA) for all evidences can be calculated. Secondly,Dempster’s rule of combination is used,and a decision-making is given. In the proposed approach,the difficulties in acquiring the BBAs are solved,the correlativity among the evidences is reduced and the subjectivity of evidences is weakened. An illustrative example in an intrusion detection shows that the two theories combination is feasible and effective.

Immune Recognition Method Based on Analogy Reasoning in Intrusion Detection System

In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic.By consuming time and resources,intrusive traffic hampers the efficient operation of network infrastructure.An effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is Intrusion Detection System(IDS).An IDS system may be host-based or network-based to monitor intrusive network activity.Finding unusual internet traffic has become a severe security risk for intelligent devices.These systems are negatively impacted by several attacks,which are slowing computation.In addition,networked communication anomalies and breaches must be detected using Machine Learning(ML).This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural Networks(ANNs).As a result,the ML model generalizes sufficiently to perform well on untried data.The NSL-KDD dataset shall be utilized for both training and testing.In this paper,we present a custom ANN model architecture using the Keras open-source software package.The specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network data.The performance of the ANN is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-Nearest Neighbors(KNN),Deep Learning(DL),Support Vector Machine(SVM),Long Short-Term Memory(LSTM),Deep Neural Network(DNN),and ANN.It is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all evaluations.This result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy in identifying and categorizing instances of network intrusion.

Cyber Security Analysis and Evaluation for Intrusion Detection Systems

Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world.Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues.It is crucial in offering a proactive security system in the field of cybersecurity.In real time,cybersecurity protects information,information systems,and networks from intruders.In the recent decade,several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches.At an increasing rate,intruders are breaching information security.Anomaly detection,software vulnerability diagnosis,phishing page identification,denial of service assaults,and malware identification are the foremost cyber-security concerns that require efficient clarifications.Practitioners have tried a variety of approaches to address the present cybersecurity obstacles and concerns.In a similar vein,the goal of this research is to assess the idealness of machine learning-based intrusion detection systems under fuzzy conditions using a Multi-Criteria Decision Making(MCDM)-based Analytical Hierarchy Process(AHP)and a Technique for Order of Preference by Similarity to Ideal-Solutions(TOPSIS).Fuzzy sets are ideal for dealing with decision-making scenarios in which experts are unsure of the best course of action.The projected work would support practitioners in identifying,prioritising,and selecting cybersecurityrelated attributes for intrusion detection systems,allowing them to design more optimal and effective intrusion detection systems.